Copyright © 2015–2018 Ulrich Bauer.

Ripser is a lean C++ code for the computation of Vietoris–Rips persistence barcodes. It can do just this one thing, but does it extremely well.

To see a live demo of Ripser's capabilities, go to live.ripser.org. The computation happens inside the browser (using PNaCl on Chrome and JavaScript via Emscripten on other browsers).

The main features of Ripser:

• time- and memory-efficient
• less than 1000 lines of code in a single C++ file
• support for coefficients in prime finite fields
• no external dependencies (optional support for Google's sparsehash)

Currently, Ripser outperforms other codes (Dionysus, DIPHA, GUDHI, Perseus, PHAT) by a factor of more than 40 in computation time and a factor of more than 15 in memory efficiency (for the example linked at live.ripser.org). (Note that PHAT does not contain code for generating Vietoris–Rips filtrations).

Input formats currently supported by Ripser:

• comma-separated values lower triangular distance matrix (preferred)
• comma-separated values upper triangular distance matrix (MATLAB output from the function pdist)
• comma-separated values full distance matrix
• DIPHA distance matrix data
• point cloud data

Ripser's efficiency is based on a few important concepts and principles, building on key previous and concurrent developments by other researchers in computational topology:

• Compute persistent cohomology (as suggested by Vin de Silva, Dmitriy Morozov, and Mikael Vejdemo-Johansson)
• Don't compute information that is never needed (for the experts: employ the clearing optimization, aka persistence with a twist, as suggested by Chao Chen and Michael Kerber)
• Don't store information that can be readily recomputed (in particular, the boundary matrix and the reduced boundary matrix)
• Take computational shortcuts (apparent and emergent persistence pairs)
• If no threshold is specified, choose the enclosing radius as the threshold, from which on homology is guaranteed to be trivial (as suggested by Greg Henselman-Petrusek)

Latest release: 1.0.1 (September 2016)

Ripser requires a C++11 compiler. Here is how to obtain, build, and run Ripser:

git clone https://github.com/Ripser/ripser.git
cd ripser
make
./ripser examples/sphere_3_192.lower_distance_matrix

Ripser supports several compile-time options. They are switched on by defining the C preprocessor macros listed below, either using #define in the code or by passing an argument to the compiler. The following options are supported:

• ASSEMBLE_REDUCTION_MATRIX: store the reduction matrix; may affect computation time but also memory usage; recommended for large and difficult problem instances
• USE_COEFFICIENTS: enable support for coefficients in a prime field
• INDICATE_PROGRESS: indicate the current progress in the console
• PRINT_PERSISTENCE_PAIRS: output the computed persistence pairs (enabled by default in the code; comment out to disable)
• USE_GOOGLE_HASHMAP: enable support for Google's sparsehash data structure; may further reduce memory footprint

For example, to build Ripser with support for coefficients:

$ c++ -std=c++11 ripser.cpp -o ripser -Ofast -D NDEBUG -D USE_COEFFICIENTS

A Makefile is provided with some variants of the above options. Use make all to build them. The default make builds a binary with the default options.

The input is given either in a file whose name is passed as an argument, or through stdin. The following options are supported at the command line:

• --format: use the specified file format for the input. The following formats are supported:
  • lower-distance (default if no format is specified): lower triangular distance matrix; a comma (or whitespace, or other non-numerical character) separated list of the distance matrix entries below the diagonal, sorted lexicographically by row index, then column index
  • upper-distance: upper triangular distance matrix; similar to the previous, but for the entries above the diagonal; suitable for output from the MATLAB functions pdist or seqpdist, exported to a CSV file
  • distance: full distance matrix; similar to the above, but for all entries of the distance matrix
  • dipha: DIPHA distance matrix as described on the DIPHA website
  • point-cloud: point cloud; a comma (or whitespace, or other non-numerical character) separated list of coordinates of the points in some Euclidean space, one point per line
• --dim k: compute persistent homology up to dimension k
• --threshold t: compute Rips complexes up to diameter t
• --modulus p: compute homology with coefficients in the prime field Z/pZ (only available when built with the option USE_COEFFICIENTS)

The following features are currently planned for future versions:

• computation of representative cycles for persistent homology (currenly only cocycles are computed)
• support for sparse distance matrices

Prototype implementations are already avaliable; please contact the author if one of these features might be relevant for your research.

Ripser is licensed under the MIT license (COPYING.txt), with an extra clause (CONTRIBUTING.txt) clarifying the license for modifications released without an explicit written license agreement. Please contact the author if you want to use Ripser in your software under a different license.